PRK-based scheduling for predictable link reliability

ABSTRACT

A Distributed sensing and control network includes multiple sensing/control nodes, each of which includes a controller. Each sensing/control node determines signal transmission/receipt scheduling based on a physical-ratio-k-scheduling (PRKS) protocol stored within the controller.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. Non-Provisional application Ser.No. 14/776,806 which was filed on Sep. 15, 2015 and is incorporatedherein by reference. U.S. Non-Provisional application Ser. No.14/776,806 is a 371 of International Patent Application No.PCT/US2014/027055 filed on Mar. 14, 2014. International PatentApplication No. PCT/US2014/027055 claims benefit of U.S. ProvisionalApplication No. 61/788,445 filed on Mar. 15, 2013.

STATEMENT REGARDING GOVERNMENT SUPPORT

This invention was made with Government support under grant number1054634 awarded by the National Science Foundation. The Government hascertain rights in the invention.

TECHNICAL FIELD

The present disclosure relates generally toward wireless communicationscheduling, and more particularly to a sensing and control networkincluding a physical-ratio-K scheduling protocol (PRKS).

BACKGROUND OF THE INVENTION

Wireless networks are utilized for real-time, closed-loop sensing andcontrol in networked cyber-physical systems. For instance, wirelessnetworking standards have been defined for industrial monitoring andcontrol, wireless networks have been deployed for industrial automation,and the automotive industry has also been exploring the application ofwireless networks to inter-vehicle as well as intra-vehicle sensing andcontrol.

In wireless networked sensing and control (WSC) systems, message passingacross wireless networks (or wireless messaging for short) allows forcoordination among distributed sensors, controllers, and actuators. Whensupporting mission-critical tasks such as industrial process controls,wireless messaging is required to be reliable (i.e., having highdelivery ratio) and to be in real-time. Current wireless messagingsystems are subject to inherent dynamics and uncertainties. Co-channelinterference is a major source of uncertainty due to collisions ofconcurrent transmissions. Thus, scheduling transmissions to preventco-channel interference is a basic element of wireless messaging in WSCsystems.

In WSC systems, not only do wireless link dynamics introduce uncertaintyas in traditional wireless sensing/control networks, dynamic controlstrategies also introduce dynamic network traffic patterns and posedifferent requirements on messaging reliability and timeliness. Foragile adaptation to uncertainties and for avoiding informationinconsistency in centralized scheduling, distributed scheduling becomesdesirable for interference control in WSC networks. Most existingsystems are either based on a physical interference model or a protocolinterference model.

In the physical model, a set of concurrent transmissions (S_(i), R_(i)),i=1 . . . N, are regarded as not interfering with one another if thefollowing conditions hold true: ((P_(S) _(i),R_(i))/(N_(i)+Σ_(j=1 . . . N,j≠i)P_(S) _(j) _(,R) _(i) ))≥γ, i=1 . . .N, where P_(S) _(i) _(,R) _(i) and P_(S) _(j) _(,R) _(i) is the strengthof signals reaching the receiver R_(i) from the transmitter S_(i) andS_(j) respectively, N_(i) is the background noise power at receiverR_(i), and γ is the signal-to-interference-plus-noise-ratio (SINR)threshold required to ensure a certain link reliability. In the protocolmodel, a transmission from a sending node S to a corresponding receivernode R is regarded as not being interfered by a concurrent transmitter Cif D_(C,R)≥K×D_(S,R), where D_(C,R) is the geographic distance between Cand R, D_(S,R) is the geographic distance between S and R, and K is aconstant number.

The physical model is a high-fidelity interference model in general, butinterference relations defined by the physical model are non-local andare combinatorial because whether one transmission interferes withanother depends on the other transmissions in the network. Even thoughmany centralized TDMA scheduling algorithms have been proposed based onthe physical model, distributed physical-model-based scheduling stillhas drawbacks. It converges slowly due to explicit network-widecoordination, it has to employ strong assumptions such as the knowledgeof node locations, it ignores cumulative interference which introducesuncertainties in communication, and it is not suitable for dynamicnetwork settings due to a need for centrally computing an interferenceset of each link (i.e., the set of links interfering with the link) orthe interference neighborhood of each link (i.e., the set of linkscausing non-negligible interference to the link). The challenge ofdesigning scheduling protocols when interfering links are beyond thecommunication range of one another is similarly not addressed inexisting physical-model-based scheduling algorithms.

Unlike the physical model, the protocol model defines local, pairwiseinterference relations. The locality of the protocol model enables agileprotocol adaptation in the presence of uncertainties. However, theprotocol model is usually inaccurate, thus scheduling based on theprotocol model or variants of the protocol model does not ensure linkreliability and also tends to reduce network throughput.

Besides scheduling based on the physical and protocol interferencemodels, distributed scheduling algorithms using general pairwiseinterference models have also been proposed. The distributed schedulingalgorithms do not address how to identify the interference set of eachlink, and their implementations assume a model similar to the protocolmodel. These algorithms do not address important systems issues such ashow to design scheduling protocols when interfering links are beyond thecommunication range of one another.

To bridge the gap between the existing interference models and thedesign of distributed, field-deployable scheduling protocols withpredictable data delivery reliability and timeliness, a major challengeis to develop an interference model that is both local and ofhigh-fidelity.

SUMMARY OF THE INVENTION

Disclosed is a distributed sensing and control network including aplurality of sensing/control nodes, each of the sensing/control nodesincluding a sensor/actuator/controller, a local memory and a wirelesstransmitter/receiver, wherein the local memory stores instructions forimplementing a physical-ratio-K-scheduling protocol (PRKS) for wirelesstransmissions, and each of the sensing/control nodes includes a localsignal map stored in the local memory. The local signal map, togetherwith the instantiated physical-ratio-K (PRK) interference models,defines an interference relationship between a sensing/control nodestoring the local signal map and each other sensing/control node of theplurality of sensing/control nodes within an exclusion region of thesensing/control node storing the local signal map.

Also disclosed is a method for scheduling wireless transmissions in adistributed sensing and control network, including the steps ofdetermining a physical-ratio-K (PRK) parameter for each link between areceiving sensing/control node and each other sensing/control node inthe distributed sensing and control network, determining an interferencerelationship between the receiving sensing/control node and each othersensing/control node in the distributed sensing and control networkusing a physical-ratio-K-scheduling protocol (PRKS) based on thedetermined physical-ratio-K parameters, and scheduling transmissions inthe distributed sensing and control network based on said interferencerelationships such that no concurrent transmissions interfere withtransmissions received at a receiving sensing/control node.

These and other features of the present invention can be best understoodfrom the following specification and drawings, the following of which isa brief description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a distributed sensing and control network utilizing aPRKS scheduling protocol.

FIG. 2 graphically illustrates a PRKS plant model instantiation.

FIG. 3 illustrates a cumulative distribution function of ΔI_(U)(t) at atypical sensing/control node.

FIG. 4a illustrates an expanding exclusion region around a receivernode.

FIG. 4b illustrates a shrinking exclusion region around a receiver node.

FIG. 5 illustrates a stability curve of an adaptive controller in thePRKS protocol.

FIG. 6 illustrates an estimation of signal power attenuation.

FIG. 7 illustrates an architecture for a PRKS system.

FIG. 8 shows an example of a proposed precomputed pipeline.

DETAILED DESCRIPTION OF AN EMBODIMENT

FIG. 1 illustrates an example distributed sensing and control network 10including multiple distributed sensors 20 and a central controller 30.Each of the distributed sensors 20 includes a wirelesstransmitter/receiver 22, and the central application controller 30includes a wireless transmitter/receiver 32. Each of the sensors 20 andthe central application controller 30 also includes a local controller24 having a memory. The memory on the local controller 24 storesinstructions for operating the corresponding remote sensor 20 or centralapplication controller 30. Included among these instructions is aphysical-ratio-K scheduling (PRKS) protocol instruction that controlsthe message scheduling from each of the sensors 20 in the mannerdescribed below. The illustrated example sensing and control network 10can be any sensing and control network having multiple sensors 20 inclose physical proximity to each other including, but not limited to, adistributed sensing and control network in an automobile, an industrialautomation system, or a fleet of vehicles.

During operation of the distributed sensing and control network 10, eachof the sensors 20 transmits information related to the sensed componentor process to the controller 30 or to other sensors 20 in thedistributed sensing and control network 10. The controller 30 utilizes acombination of data from the sensors 20 to determine appropriateresponses and transmits the responses to actuators 40 within the system.In alternate distributed sensing and control networks 10, some or all ofthe sensors 20 can include actuators or other responsive components andreceive instructions from the central controller 30. In a practicalimplementation, some, or all, of the sensors 20 communicate directlywith at least one other sensor 20 without transmitting through thecontroller 30. Each of the sensors 20 and the controller 30 arealternately referred to below as a “node”.

Due to the proximity of the sensors 20 with each other, transmissionfrom a sensor 20 can interfere with transmissions received by othersensors 20, should the transmissions temporally overlap. In order toprevent this overlap and interference, a robust message schedulingprotocol based on a physical-ratio-K (PRK) model is implemented in eachof the sensors 20. The PRK-based scheduling protocol is referred to as aPRKS protocol.

A physical-ratio-K (PRK) interference model that integrates the protocolmodel's locality with the physical model's high-fidelity has beenproposed to model expected interference. Given a transmission from asending node S to a receiving node R, a concurrent transmitter C isregarded as not interfering with the reception at R in the PRK model if,and only if, the following holds true: P (C, R)<(P (S, R))/(K_(S,R,T)_(S,R) ) where P (C, R) and P (S, R) is the strength of signals reachingR from C and S respectively, K_(S,R,T) _(S,R) is the minimum real number(i.e., can be non-integer) chosen such that, in the presence ofinterference from all concurrent transmitters, the probability for nodeR to successfully receive packets from node S is at least T_(S,R).T_(S,R) is the minimum link reliability required by applications (e.g.,control algorithms).

Unlike the physical model, the PRK model is based on locally measurableand locally controllable metrics and only pairwise interferencerelations between close-by nodes need to be defined in the model. Unlikethe protocol model, the PRK model is of high-fidelity because the PRKmodel captures the properties and constraints of wireless communication(including cumulative interference, anisotropy, and asymmetry) byensuring the required link reliability in scheduling and by using signalstrength instead of geographic distance in model formulation. PRK-basedscheduling also helps reduce data delivery delay by minimizing the needfor packet retransmissions. Furthermore, PRK-based scheduling can enablea throughput very close to (e.g., >95%) what is feasible inphysical-model-based scheduling while ensuring application-requiredreliability. Therefore, the PRK model bridges the gap between thesuitability for distributed implementation and the enabled schedulingperformance in existing interference models.

Existing forays into the PRK model have ignored the practical problem ofPRK-based scheduling for real-world applications.

To realize distributed PRKS scheduling in a practical application, suchas the distributed sensing and control network 10 of FIG. 1, thefollowing points are addressed:

First, the parameter K_(S,R,T) _(S,R) of the PRK model depends on thespecific link (sending node S, receiving node R), the applicationrequirement on the link reliability (i.e., the targeted link reliabilityT_(S,R)), as well as the network and environmental conditions such astraffic pattern and wireless path loss, which can be dynamic andunpredictable. Thus, the practical PRKS system instantiates the PRKmodel parameter K_(S,R,T) _(S,R) on the fly in each node based onin-situ application requirements as well as network and environmentalconditions.

Second, given a link (S, R) between a sending node S and a receivingnode R and a specific instantiation of the PRK model, the parameterK_(S,R,T) _(S,R) defines an exclusion region E_(S,R,T) _(S,R) around thereceiving node R (the wireless transmitter/receiver 22 of the receivingsensor 20) such that a concurrent node C is within E_(S,R,T) _(S,R) ifand only if P (C, R)≥(P(S,R)/K_(S,R,T) _(S,R) ) where P (C, R) is thestrength of data signals reaching the receiving node R from theconcurrent node C. Every node C within the exclusion region is preventedfrom transmitting concurrently with the reception at node R. It isdifficult to ensure this property due to large interference range,anisotropy and asymmetry in wireless communication, as well as the delayin protocol signaling.

In PRKS, the problem of identifying the PRK model parameter K_(S,R,T)_(S,R) is designed as a minimum-variance regulation control problem.Distributed controllers 24 within the sensors 20 and the centralapplication controller 30 allow each sensor 20 and the centralapplication controller 30 to adapt a stored PRK model parameter forensuring the desired link reliability through purely local coordination.To ensure that sensors 20 and the central application controller 30interfering with one another (as defined by the PRK model) do nottransmit concurrently, each sensor 20 and the central applicationcontroller 30 includes a local signal map stored in local memory of thesensor/application-controller, the local signal map allows sensors 20and the central application controller 30 close-by to maintain thewireless path loss among themselves. Together with the PRK model andtransmission power control in protocol signaling, local signal mapsenable the sensors 20 and the central application controller 30 toprecisely identify the interference relations among themselves despitethe anisotropic, asymmetric wireless communication and largeinterference range of the distributed sensor system 10. To address anyinherent delay in protocol signaling and to avoid interference betweenprotocol signaling and data transmissions, the PRKS protocol decouplesprotocol signaling from data transmissions and schedules datatransmissions in a TDMA fashion.

The local controllers 24 in the sensors 20 and the central applicationcontroller 30 enable network-wide convergence to a state where desiredlink reliabilities are ensured. Unlike existing scheduling protocolswhere link reliability can be as low as 2.49%, the PRKS protocol enablespredictably high link reliability (in one example the high linkreliability is at least 95%) in different network and environmentalconditions without a priori knowledge of the network and environmentalconditions.

In this disclosure, we consider mostly-static wireless sensing andcontrol (WSC) networks such as those in industrial monitoring andcontrol. For explanatory purposes, we assume that background noise powerand wireless path loss are mostly static and do not change within veryshort timescales (e.g., within a few milliseconds duration of a fewpacket transmissions), that the links are chosen such that their packetdelivery reliabilities are above the required packet deliveryreliability in the absence of interference, and that data packets aretransmitted using a fixed power. In what follows, we first describe acontrol-theoretic approach to instantiating the PRK model, then wepresent local signal maps, such as would be stored in the sensors 20 inthe distributed sensing and control network 10 as the basis for protocolsignaling. Next, we present the protocol PRKS for distributed PRK-basedscheduling. Finally, we present methods for extending PRKS for broadcasttransmissions and scheduling in mobile networks. As described above,each of the sensors 20 in the distributed sensing and control network 10includes a controller having a memory storing the PRKS protocol. Table 1defines some major notations used in this disclosure.

TABLE 1 Y_(S, R) (t ) Measured packet delivery rate for link (S, R) attime t. P(S, R, t) Expected power, in units of mW, of data packetsignals reaching R from S at time t; assumed to be mostly static atshort timescales. P_(S, R)(t) Expected power, in units of dBm, of datapacket signals reaching R from S at time t; I_(R)(t) Sum of backgroundnoise power and interference power at receiver R at time t. f(.) Thefunction modeling the relation between packet delivery rate and SINR.a(t) f′(P_(S, R)(t) − I_(R)(t)). b(t) f(P_(S, R)(t) − I_(R)(t)) −(P_(S, R)(t) − I_(R)(t))f′ (P_(S, R)(t) − I_(R))(t). y(t) Smoothed linkreliability measurement, i.e., y(t) = cy(t − 1) + (1 − c)Y_(S, R)(t) cParameter of the EWMA filter in feedback loop. ΔI_(R)(t) Computedcontrol input at time instant t. ΔI_(U)(t) Change of interference fromoutside the exclusion region of R from time t to t + 1. μ_(U)(t) Mean ofΔI_(U)(t). σ_(U) ²(t) Variance of ΔI_(U)(t). K_(S, R, T) _(S,) _(R) (t)PRK model parameter for link (S, R) at time t.

 _(S, R, T) _(S, R) (t) $\quad\begin{matrix}{{{Exclusion}\mspace{14mu}{region}\mspace{14mu}{around}\mspace{14mu}{receiver}\mspace{14mu} R\mspace{14mu}{at}\mspace{14mu}{time}\mspace{14mu} t};{a\mspace{14mu}{node}}} \\{C \in {{{{\mathbb{E}}_{S,R,T_{S,R}}(t)}\mspace{14mu}{{iff}.\mspace{14mu}{P_{C,R}(t)}}} \geq {\frac{P_{S,R}(t)}{K_{S,R,T_{S,R}}(t)}.}}}\end{matrix}$ P_(C, R)′ Average signal power attenuation from a node Cto another node R; maintained in nodes' local signal maps. d_(R, S)Delay in the receiver R sharing the latest PRK model parameter of link(S, R) with the transmitter S. d_(S, R, C, D)′ Delay in a node S sharingits knowledge of the latest PRK model parameter of link (C, D) withanother node R.

Given a link (S, R), the task of instantiating the PRK model in thereceiving node R is to determine the parameter K_(S,R,T) _(S,R) suchthat the resulting scheduling ensures the required minimum linkreliability T_(S,R). In a practical implementation, the PRK modelinstantiation problem is formatted as a classical regulation controlproblem, where the “reference input” is the required link reliabilityT_(S,R), the “output” is the actual link reliability Y_(S,R) from thesending node S to the receiving node R, and the “control input” is theparameter K_(S,R,T) _(S,R) .

To solve this control design problem, one challenge is the difficulty incharacterizing the “plant model” on the relation between control inputK_(S,R,T) _(S,R) and control output Y_(S,R). This difficulty arisesbecause the relation is complex and depends on network and environmentalconditions which are unpredictable in the practical distributed sensingand control network 10. It is noted that the outcome of changing thecontrol input K_(S,R,T) _(S,R) is the change in the interference powerat receiver R. The change in interference power, denoted by ΔI_(R), isthe actual control input in control algorithm design. Thus, existingcommunication theory is leveraged to derive the plant model on therelation between Y_(S,R) and ΔI_(R) as follows.

I_(R)(t) denotes, in units of dBm, the sum of the background noise powerand the power of all interfering signals at the receiver R at timeinstant t. P_(S,R)(t) denotes the received data signal power P(S, R) inunits of dBm at time instant t. Given a modulation and coding scheme,communication theory determines the following relationship, referred toas the “theoretical model”: Y_(S,R)(t)=f(P_(S,R)(t)−I_(R)(t)), where fis a non-decreasing function, and P_(S,R)(t)−I_(R)(t) represents theSINR in dB at time instant t. For IEEE 802.15.4-compatible radios suchas Chipcon CC2420, for instance,

${{Y_{S,R}(t)} = \left( {1 - {\frac{8}{15} \times \frac{1}{16} \times {\sum\limits_{k = 2}^{16}\;{\left( {- 1} \right)^{k}\begin{pmatrix}16 \\k\end{pmatrix}e^{({20 \times {({{P_{S,R}{(t)}} - {I_{R}{(t)}}})} \times {({\frac{1}{k} - 1})}})}}}}} \right)^{8\ell}},$

where l is the packet length in units of bytes. Given that the functionf is usually non-linear, and to address this challenge of non-linearcontrol, the PRKS protocol approximates function f using multiple linearfunctions, and depending on the current operating point of the system,uses self-tuning regulators to adapt controller behavior. Given the SINRP_(S,R)(t)−I_(R)(t) at time instant t (t=1, 2, . . . ), morespecifically, we approximate function f with the following linearfunction, referred to as “the linear model”:Y _(S,R)(t)−a(t)(P _(S,R)(t)−I _(R)(t))+b(t)

where a(t) is the derivative of function f when the SINR isP_(S,R)(t)−I_(R)(t), i.e., a(t)=f′(P_(S,R)(t)−I_(R)(t)), and b(t)=f(P_(S,R)(t)−I_(R)(t))−(P_(S,R)(t)−I_(R)(t))f′(P_(S,R)(t)−I_(R)(t)).Assuming that the background noise power is the same from time t to t+1,I_(R)(t+1) may differ from I_(R)(t) for two possible reasons:

First, from time t to t+1, the PRK model parameter may change fromK_(S,R,T) _(S,R) (t) to K_(S,R,T) _(S,R) (t+1). Accordingly, theexclusion region around the receiver R changes from E_(S,R,T) _(S,R) (t)to E_(S,R,T) _(S,R) (t+1). If K_(S,R,T) _(S,R) (t+1)>K_(S,R,T) _(S,R)(t), nodes 20, 30 in E_(S,R,T) _(S,R) (t) but not in E_(S,R,T) _(S,R)(t+1) can introduce interference to the receiving node R at time t butnot at time t+1. Similarly, when K_(S,R,T) _(S,R) (t+1)<K_(S,R,T) _(S,R)(t), nodes 20, 30 in E_(S,R,T) _(S,R) (t+1) but not in E_(S,R,T) _(S,R)(t) can introduce interference to the receiving node R at time t+1 butnot at time t. ΔI_(R)(t) denotes the interference change at thereceiving node R due to the change of the PRK model parameter from t tot+1. ΔI_(R)(t) can be controlled by the receiver R, thus it is the“control output.”

Second, the set of nodes 20, 30 that are not in the exclusion regionaround the receiver node R but transmit concurrently with the link (S,R) may change from time t to t+1. Accordingly, the interferenceintroduced by the nodes 20, 30 outside the exclusion region around Rchanges from t to t+1, ΔI_(U) (t) denotes this change. ΔI_(U) (t) cannotbe controlled by the receiving node R, and ΔI_(U) (t) is the“disturbance” to the system. The mean and variance of ΔI_(U) (t) aredenoted as μ_(U) (t) and σ² _(U)(t) respectively.

Considering both of the above possible reasons for I_(R)(t) andI_(R)(t+1) being different, we get the equation:I_(R)(t+1)=I_(R)(t)+ΔI_(R)(t)+ΔI_(U) (t), where ΔI_(R)(t) and ΔI_(U) (t)are in units of dB.

Using the linear approximation of function f as shown byY_(S,R)(t)=a(t)(P_(S,R)(t)−I_(R)(t))+b(t) at time t, the predicted linkreliability for time t+1 calculates as follows and is referred to as the“plant” model: Y_(S,R)(t+1)=a(t)(P_(S,R)(t+1)−I_(R)(t+1))+b(t).Therefore, the “plant model” for link (S, R) at time t isI_(R)(t+1)=I_(R)(t)+ΔI_(R)(t)+ΔI_(U) (t) andY_(S,R)(t+1)=a(t)(P_(S,R)(t+1)−I_(R)(t+1))+b(t), where I_(R)(⋅) andY_(S,R)(⋅) are the “state” and the “output” of the plant respectively.To deal with the noise in measuring Y_(S,R)(⋅), the PRKS protocol usesan exponentially-weighted-moving-average (EWMA) filter with a weightfactor c (0≤c<1) in the feedback loop. FIG. 2 graphically illustratesthe system model described above, using anexponentially-weighted-moving-average (EWMA) filter of:y(t)=cy(t−1)+(1−c)Y_(S,R) (t)=cy(t−1)+(1−c)[a(t−1)(P_(S,R) (t)−I_(R)(t))+b(t−1)].

Given the probabilistic nature of wireless communication and the randomdisturbance ΔI_(U) (⋅) from outside the exclusion region of thereceiving node R, the measured link reliability y(t) is expected to beinherently random. Thus the goal is to minimize the variance of y(t)while making sure that the mean value of y(t) is no less than therequired link reliability. More formally, the control design at time tchooses a control input ΔI_(R)(t) that minimizes the variance of y(t+1)while ensuring that E[y(t+1)]=T_(S,R)+δ_(Y) (δ_(Y)>0), where T_(S,R) isthe required link reliability, and δ_(Y) controls the probability fory(t)<T_(S,R). For a minimum-variance regulation control problem, thecontrol input that minimizes var[y(t+1)] while ensuringE[y(t+1)]=T_(S,R)+δ_(Y) isΔI _(R)(t)=(((1+c)y(t)−cy(t−1)−T _(S,R)−δ_(Y))/((1−c)a(t)))−μ_(U)(t)

And the minimum value of var[y[t+1]] isσ² _(y,min)(t+1)=(1−c)² a(t)²σ² _(u)(t)

The above relationship is referred to as the minimum variancecontroller.

Given the uniformly random nature of the set of concurrent transmittersoutside the exclusion region around the receiving node R, μ_(U)(t) tendsto be zero in PRKS protocol based systems. In an example sensor 20, forinstance, μ_(U) (t)=−0.00005 dB with a 95% confidence interval of[−0.0453 dB, 0.0452 dB]. Additionally, in a practical implementation ofthe PRKS protocol ΔI_(U) (t) typically centers around 0 dB and lieswithin [−1 dB, 1 dB]. This relationship is referred to as a cumulativedistribution function and is illustrated in FIG. 3. For practicalimplementations of PRKS scheduling, μ_(U) (t)≈0.

With the minimum variance controller, the expected link reliability fora link (S, R) is guaranteed to be at least the required link reliabilityT_(S,R). In addition, link reliability undershoot probability iscontrolled, and defined as:

${\Pr\left\{ {{y\left( {t + 1} \right)} \leq T_{S,R}} \right\}} \leq {\frac{\left( {1 - c} \right)^{2}{a(t)}^{2}{\sigma_{U}^{2}(t)}}{\delta_{Y}^{2}}.}$

Thus, the undershoot probability is controlled by tuning parameters cand δ_(Y). In some examples, the undershoot probability is also tuned bycontrolling σ² _(U)(t), which reflects the variability of interferencefrom outside the exclusion region of the receiving node R. In onepractical implementation of the presently disclosed PRKS protocol, welet δ_(Y)=0 and c=15/16 from ΔI_(R)(t) to K_(S,R,T) _(S,R) (t+1).

Given that it is convenient for the receiving node R to measure linkreliability Y_(S,R)(t), the minimum-variance controller is stored in thememory of each node 20, 30 and executed at the node 20, 30. After thenode 20, 30 computes the control input ΔI_(R)(t) at time t, the node 20,30 computes K_(S,R,T) _(S,R) (t+1) so that:

$\left\{ {\begin{matrix}{{{K_{S,R,T_{S,R}}\left( {t + 1} \right)} = {K_{S,R,T_{S,R}}(t)}},} & {{{if}\mspace{14mu}\Delta\;{I_{R}(t)}} = 0} \\{{{K_{S,R,T_{S,R}}\left( {t + 1} \right)} > {K_{S,R,T_{S,R}}(t)}},} & {{{if}\mspace{14mu}\Delta\;{I_{R}(t)}} < 0} \\{{{K_{S,R,T_{S,R}}\left( {t + 1} \right)} < {K_{S,R,T_{S,R}}(t)}},} & {{{if}\mspace{14mu}\Delta\;{I_{R}(t)}} > 0}\end{matrix}\quad} \right.$

and that |ΔI_(R)(t)| is equal to the expected interference that thenodes in either E_(S,R,T) _(S,R) (t) or E_(S,R,T) _(S,R) (t+1), but notin both, introduce to the sensor 20 when the PRK model parameter ismin{K_(S,R,T) _(S,R) (t), K_(S,R,T) _(S,R) (t+1)}. To realize this, theexpected interference I_(C,R)(t) that the concurrent node C introducesto the receiving node R when the concurrent node C is not in theexclusion region of the receiving node R for each concurrent node C isdefined in the local region around the receiving node R. Based on thisdefinition, then, I_(C,R)(t)=β_(C)(t)P_(C,R)(t), where β_(C) (t) is theprobability for the concurrent node C to transmit data packets at time tand P_(C,R)(t) is the power strength of the data signals reaching thereceiving node R from the concurrent node C. As is discussed belowP_(C,R)(t) and β_(C)(t) can be estimated through purely localcoordination between the receiving node R and the concurrent node C.Considering the discrete nature of node distribution in space and therequirement on satisfying the minimum link reliability T_(S,R), thefollowing rules for computing K_(S,R,T) _(S,R) (t+1) are utilized in thePRKS control protocol:

When ΔI_(R)(t)=0, let K_(S,R,T) _(S,R) (t+1)=K_(S,R,T) _(S,R) (t).

When ΔI_(R)(t)<0 (i.e., when the exclusion region should be expanded,FIG. 4a ), let E_(S,R,T) _(S,R) (t+1)=E_(S,R,T) _(S,R) (t), then keepadding nodes not already in E_(S,R,T) _(S,R) (t+1), in thenon-increasing order of their data signal power at R, such that adding Binto E_(S,R,T) _(S,R) (t+1) makes: ΣC∈

_(S,R,T) _(S,R) (t+1)\

_(S,R,T) _(S,R) (t)^(I) _(C,R)(t)≥|ΔI_(R)(t)| for the first time. Thenlet

${K_{S,R,T_{S,R}}\left( {t + 1} \right)} = {\frac{P\mspace{14mu}\left( {S,R,t} \right)}{P\mspace{14mu}\left( {B,R,t} \right)}.}$

When ΔI_(R)(t)>0 (i.e., when the exclusion region should be shrunk, FIG.4b ), let E_(S,R,T) _(S,R) (t+1)=E_(S,R,T) _(S,R) (t),then keep removingnodes in E_(S,R,T) _(S,R) (t+1), in the non-decreasing order of theirdata signal power at R, out of E_(S,R,T) _(S,R) (t+1) until the node Bsuch that removing any more nodes after removing B makes ΣC∈

_(S,R,T) _(S,R) (t)\

_(S,R,T) _(S,R) (t+1)^(I)C,R(t)>|ΔI_(R)(t)| for the first time. Then let

${K_{S,R,T_{S,R}}\left( {t + 1} \right)} = {\frac{P\mspace{14mu}\left( {S,R,t} \right)}{P\mspace{14mu}\left( {B,R,t} \right)}.}$

FIG. 4a illustrates the above described concept for cases ΔI_(R)(t)<0with the exclusion region increasing. FIG. 4b illustrates the abovedescribed concept for cases of ΔI_(R)(t)>0 with the exclusion regiondecreasing. The exclusion region is defined by lines B and C and definesthe approximate signal strength region around a receiving sensor R inwhich concurrent transmissions are excluded. In a practicalimplementation, sensors 20 are within or outside of the exclusion regionbased on signal strength and not strictly based on relative geographicpositioning. In one example, the initial value of the PRK modelparameter is set such that the initial exclusion region around Rincludes every node whose transmission alone, concurrent with thetransmission from S to R, can make the link reliability drop belowT_(S,R).

The controller designs and analysis based on the linear model are moreaccurate when y(t) is closer to T_(S,R). When y(t) is far away fromT_(S,R), directly using the linear model can lead to significantundershoot or overshoot in feedback control. FIG. 5 illustrates astability curve of an adaptive controller.

Assuming the target operating point is A where the link reliability isT_(S,R) in FIG. 5, for instance, applying the linear model and theminimum variance controller when the operating point is B at time tresults in E[y(t+1)]=T_(B′), which is significantly lower than T_(S,R)and leads to significant undershoot. Similarly, applying the linearmodel and the minimum variance controller when the operating point is Cat time t results in E[y(t+1)]=T_(C′), which is significantly higherthan T_(S,R) and thus lead to significant overshoot. Significantundershoot or overshoot may lead to network-wide instability in feedbackcontrol due to the coupling between individual links via ΔI_(U) (t). Forstability and for avoiding significant undershoot and overshoot incontrol, a(t) is replaced with a refined version a_(r)(t) (definedbelow) in some practical controller implementations.

${a_{r}(t)} = \left\{ \begin{matrix}{{a(t)},} & {{{if}\mspace{14mu}{{{y(t)} - T_{S,R}}}} \leq e_{0}} \\{a_{0},} & {{{if}\mspace{14mu}{{{y(t)} - T_{S,R}}}} > e_{0}}\end{matrix} \right.$

where e₀ is a threshold value for the linear model to be accurate aroundthe neighborhood of T_(S,R), and

$a_{0} = {\frac{T_{S,R} - {y(t)}}{{f^{- 1}\left( T_{S,R} \right)} - {f^{- 1}\left( {y(t)} \right)}}}$is the gradient of the line connecting the current operating point y(t)and the target point T_(S,R) on function f. Letting a(t)=a₀ when|y(t)−T_(S,R)|>e₀ avoids overshoot and undershoot in the feedbackcontrol of K_(S,R,T) _(S,R) (⋅) at link (S, R), thus preventingY_(S,R)(⋅) from oscillating around T_(S,R) for a given disturbanceΔI_(U) (⋅) and enabling network-wide convergence in the regulationcontrol.

Note that the functional form of f in the theoretical model, and thusits gradient, are more stable than the specific realization of f (e.g.,specific mapping between Y_(S,R) and P_(S,R)−I_(R)) across differentnetwork and environmental conditions; hence letting a_(r)(t) be a(t)instead of a₀ when |y(t)−T_(S,R)|≤e₀ helps address the inaccuracy of thetheoretical model in practice. In one practical implementation, e₀ is5%.

Turning our attention now to the development of a local signal map foreach node 20, 30 in the distributed sensing and control network 10illustrated in FIG. 1, given a link (S, R) and a specific instantiationof the PRK model in the controllers 24 of the sensors 20 and thecontroller 30, the parameter K_(S,R,T) _(S,R) (t) defines the exclusionregion E_(S,R,T) _(S,R) (t) around the receiver R such that a concurrentnode C is within the exclusion region if, and only if,

${P\mspace{14mu}\left( {C,R,t} \right)} \geq {\frac{P\mspace{14mu}\left( {S,R,t} \right)}{K_{S,R,T_{S,R}}(t)}.}$In theoretical PRKS protocol scheduling, every concurrent node withinthe exclusion region is aware of its existence in the exclusion regionand does not transmit concurrently with the reception at the receivingnode R. Yet it is difficult to ensure this property due to the followingreal-world complexities in wireless communication: 1) the concurrentnode C may be located beyond the communication range of the receivingnode R such that the receiving node R cannot inform the concurrent nodeC about its state (e.g., the value

$\left. \frac{P\mspace{14mu}\left( {S,R,t} \right)}{K_{S,R,T_{S.R}}(t)} \right)$with the regular data transmission power. 2) Wireless communications maybe anisotropic such that it is difficult for the receiving node R totransmit protocol signaling messages (e.g., a CTS-type message) thatreaches, and only reaches, nodes within the exclusion region. 3)Wireless communications may be asymmetric such that nodes interferingwith one another may not know one another's state (e.g.,

$\frac{\overset{\_}{P}\mspace{14mu}\left( {S,R,t} \right)}{K_{S,R,T_{S,R}}(t)},$receiving or idle).

To address these challenges, in the instantiated PRKS protocol of FIG.1, every node R maintains a local signal map within a local memory thatcontains the average signal power attenuation between node R and everyconcurrent node C close-by. To measure the signal power attenuation, P′,from a concurrent node C to another node R, the concurrent node Cinforms node R of its transmission power P_(C) by piggybacking theinformation onto its packets to node R, and then node R derives thepower attenuation as long as node R can estimate the power of thereceived signals from the concurrent node C, denoted by P_(C,R). To thisend, node R can sample the RSSI value P_(total) at an instantimmediately before finishing receiving a packet from the concurrent nodeC. Immediately after receiving the packet, node R samples the RSSI valueP_(I) again.

FIG. 6 illustrates an estimation of signal power attenuation. As shownin FIG. 6, P_(I) is the sum of the background noise power and theinterference power at R right after the packet reception, andP_(total)=P_(C,R)+P_(I)′ where P_(I)′ is the sum of the background noisepower and the interference power at R right before the packet reception.The signal maps are maintained in the control plane of the PRKS protocolwhere wireless channel access is based on a known random access methodCSMA/CA. Given that P_(total) and P_(I) can be sampled at a very shortinterval (e.g., less than 0.01 milliseconds), and that the backgroundnoise power as well as the interference power do not change much in suchshort intervals in CSMA/CA-based wireless networks, the sum of thebackground noise power and the interference power do not significantlychange immediately before and immediately after a packet reception. Inother words, P_(I)′ and P_(I) are approximately the same.

Thus, P_(C,R)=P_(total)−P_(I)′ is approximately equal toP_(total)−P_(I). Once the receiving node R acquires a sample of P_(C,R),node R can compute a sample of P′_(C,R) as P′_(C,R)=P_(C)−P_(C,R).

In this manner, node R can get a series of samples of P′_(C,R) and thenuse these samples to derive the average signal power loss from theconcurrent node C to itself. Using the above method, nodes close-by canestablish their local signal maps through purely local sampling of theirpacket receptions without any global coordination in the network, andthe local signal maps generated in this manner are accurate.

Note that the local signal map maintains power attenuation from aconcurrent node C to a receiving node R instead of simply the receptionpower of signals from node C to node R so that the local signal map canbe used to estimate the reception power of signals that are transmittedat different powers (e.g., for the control signals of protocol PRKS).For protocol signaling in PRKS, the local signal maps also maintainbi-directional power attenuation between a pair of close-by nodes. Afterestimating P′_(C,R), for instance, the receiving node R also informs theconcurrent node C of P′_(C,R) so that node C is aware of the powerattenuation from itself to node R.

Turning our attention now to protocol signaling based on signal maps,the local signal map at node R records signal power attenuation betweennode R and the nodes close-by. Using this information and knowntransmission power control algorithms, node R can broadcast signalingpackets at an appropriate power level such that these packets with thevalue of

$\frac{P\mspace{14mu}\left( {S,R,t} \right)}{K_{S,R,T_{S,R}}(t)}$can be received with high probability by all the nodes in the exclusionregion (E_(S,R,T) _(S,R) (t)) around node R. This is accomplished evenif a concurrent node C within the exclusion region is beyond the regulardata communication range of node R, in which case, the broadcast packetsare transmitted at a power higher than the regular data transmissionpower. Therefore, the local signal map addresses the challenge of largeinterference range through transmission power control.

To further increase the reliability of protocol signaling, node R canbroadcast each signaling packet multiple times, and nodes in theexclusion region of node R can rebroadcast the signaling packet theyhear from node R. To reduce the delay in information sharing, signalingpackets with fresher information (i.e., information that has beentransmitted for fewer number of times) also have higher priorities inchannel access by using smaller contention windows in CSMA/CA.

When a concurrent node C receives the signaling packet from node R, nodeC can use its local signal map to decide whether the transmission mayinterfere with the transmission from a sending node S to a receivingnode R (i.e., whether the concurrent node C is within the exclusionregion) by checking whether

${P\mspace{14mu}\left( {C,R,t} \right)} \geq {\frac{P\mspace{14mu}\left( {S,R,t} \right)}{K_{S,R,T_{S,R}}(t)}.}$Therefore, the signaling packets can reach nodes not in the exclusionregion without falsely including those nodes into the exclusion region,thus addressing the challenge of anisotropic wireless communication.Similarly, using power control algorithms and local signal maps, a pairof nodes C and R can inform each other of their respective states (e.g.,the PRK model parameter) using different transmission powers forsignaling packets, thus addressing the challenge of asymmetric wirelesscommunication in protocol signaling.

For the correctness of the above protocol signaling method, the signalmap of a node R includes a set E′ of nodes whose transmission mayinterfere with the reception at R or whose reception may be interferedby the transmission of R. E′ is the set of nodes within the exclusionregion of node R. Since the set E′ is dynamic and uncertain depending onnetwork and environmental conditions, the node R dynamically adjusts theset of nodes in its local signal map through local coordination withnodes close-by, and node R may also maintain a relative large signal mapto include the nodes that may be in E′ over time. Together with the PRKmodel instantiation method discussed above, the above field-deployablesignaling mechanisms enable agile, high-fidelity identification ofinterference relations among nodes, thus bridging the gap between thetheory of pairwise-interference-model-based scheduling and the practicalimplementation of these algorithms.

Two basic tasks of PRKS protocol in interference control are 1) enablingnodes to be accurately aware of the mutual interference relations amongthemselves and 2) controlling channel access so that no two interferinglinks use the same wireless channel at the same time. These tasks makethe commonly used single-channel contention-based approach unsuitablefor PRKS protocol interference control for the following reasons:

First, in contention-based channel access control, each datatransmission is preceded by a protocol signaling phase either implicitlythrough carrier sensing or explicitly through a RTS-CTS handshake. Dueto the probabilistic nature of wireless communication and thepotentially large interference range, it is difficult to make suchper-transmission protocol signaling predictably reliable even with themechanisms discussed above. Accordingly, it is difficult for nodes to beaccurately aware of their mutual interference relations and oneanother's operation states (e.g., transmission or not), and it is thusdifficult to control interference between the nodes in a predictablemanner.

Second, even if the per-transmission protocol signaling is morepredictably reliable through mechanisms such as retransmission ofsignaling packets, this introduces significant delay and overhead foreach data transmission. Even worse, the signaling packets may betransmitted at relatively higher power to ensure coverage of thepotentially large exclusion regions, and the high-power transmissions ofsignaling packets introduce significant interference to the datatransmissions themselves. In trying to ensure the required data deliveryreliability in the presence of strong interference from protocolsignaling, nodes will adapt their PRK model parameter to expand theirindividual exclusion regions, which in turn requires the signalingpackets to be transmitted at even higher power and thus leads to systeminstability.

To address the aforementioned challenges, protocol signaling isdecoupled from data transmission. Given a link (S, R), highly accurateestimation of its reliability usually requires the knowledge of thetransmission status of several data transmissions along (S, R). In someexamples, estimation of reliability requires knowledge of thetransmission status of as many as 20 data transmissions. Accordingly, ittakes time to get a new link reliability feedback, and the timescale ofPRK model adaptation as well as the resulting change in interferencerelations between (S, R) and close-by nodes/links is much longer thanthe timescale of individual data transmissions along (S, R). After eachPRK model adaptation, the receiver R can inform the relevant nodes ofthe new value of parameter K_(S,R,T) _(S,R) and thus the correspondingchange in interference relations using the protocol signaling mechanismsdiscussed above. Each new value of K_(S,R,T) _(S,R) can be reliably andquickly signaled within 1.4 transmissions of the signaling packet onaverage. Therefore, instead of requiring perfectly reliable signalingfor each data transmission as in contention-based channel accesscontrol, protocol signaling is treated as an independent process whichensures timely awareness of the mutual interference between nodes/links.Based on the latest information of mutual interference relations, datatransmissions can be scheduled in a TDMA fashion without being coupledwith protocol signaling. Furthermore, the periodic sampling of physicalprocesses in WSC networks also makes TDMA an efficient schedulingmechanism as compared with contention-based approaches.

Besides enabling precise awareness of mutual interference relations, thedecoupling of protocol signaling and data transmission also enables thetransmission of signaling packets and data packets in different wirelesschannels, thus avoiding the interference between protocol signaling anddata transmission as well as the corresponding system instability. Thewireless channels used for protocol signaling and data transmission arereferred to as the control channel and the data channel respectively.Using a control channel is necessary for avoiding system instabilitywhile addressing the challenges of protocol signaling at the same time.Since protocol signaling does not introduce high traffic load, it maywell be able to reuse the control channel that has been set aside inindustry standards such as IEEE 1609.4.

Based on the above principles, FIG. 7 illustrates a scheme for apractical PRKS protocol based system. To address the above challenges,PRKS separates the functionalities of PRK-based channel access controlinto control plane functions and data plane functions as shown in FIG.7. In the control plane, the sender node S and the receiver node R of agiven link (S, R) learn the set of links whose transmissions cannot takeplace concurrently with the transmission from node S to node R throughthe protocol signaling mechanisms presented above. This set of links isdefined as the conflict set of link (S, R). More specifically, a link(C, D) is in the conflict set of (S, R) and thus conflicting with (S, R)at a time instant t if node C is within the exclusion region of node Ror node S is within the exclusion region of node D.

Based on the conflict sets of links, data transmissions along individuallinks can be scheduled in a distributed, TDMA manner according to aLink-Activation-Multiple-Access (LAMA) algorithm. With the LAMAalgorithm, the link (S, R) is regarded as active in a time slot if nodeS transmits to node R in the slot. Given a time slot, the sender node Sand the receiver node R of link (S, R) first compute the priorities forthe link (S, R) and the links in the conflict set of (S, R) to be activein the time slot, then S decides to transmit to R and R decides toreceive data from S if and only if, for this time slot, (S, R) hashigher priority to be active than every conflicting link.

Every node in the distributed sensing and control network computes linkactivation priorities in the same manner such that no two conflictinglinks will be active in the same time slot as long as links areaccurately aware of their mutual interference relations. If a link (S,R) is active in a time slot, node S will transmit data packet(s) to nodeR in this time slot. The status (i.e., successes or failures) of datatransmissions in the data plane are fed back into the control plane forestimating the in-situ link reliabilities, which in turn triggers PRKmodel adaptation and then the adaptation of the TDMA transmissionscheduling accordingly. In the control plane, nodes also leverage thetransmissions and receptions of protocol signaling packets to maintaintheir local signal maps as disclosed above. Given that the instantiatedPRK models precisely identify the conflict sets of individual links, theTDMA scheduling in the PRKS protocol also eliminates hidden terminalsand exposed terminals.

To avoid interference between protocol signaling transmissions and datatransmissions, protocol signaling packets and data packets aretransmitted in different wireless channels, regarded as the controlchannel and the data channel respectively. By default, a node stays inthe control channel. At the beginning of a time slot, every nodeexecutes the OLAMA scheduling algorithm to check whether any of itsassociated links will be active in this time slot.

If one of the associated links is active in this time slot, the nodeswitches to the data channel for data transmission or receptiondepending on whether the node is the transmitter or receiver of theassociated active link. After the data transmission/reception, the nodeswitches back to the control channel, and, if the node is a receiver ofa link in this time slot, the node feeds back the status (i.e., successor failure) of this transmission to the control plane for linkreliability estimation and the corresponding PRK model adaptation if anew link reliability estimate is generated. If the node is not involvedin any data transmission/reception in the time slot, the node stays inthe control channel, and tries to access the control channel viaCSMA/CA.

If the node wins channel access (e.g., sensing the channel as idle), thenode transmits a signaling packet including information on the PRK modelparameters for all of the node's associated links and their conflictinglinks. If the node does not win channel access, the node stays in thecontrol channel receiving signaling packets from other nodes and performfunctions related to signal map maintenance and protocol signaling. Thelength of a time slot is pre-determined such that the aforementionedactions can be completed in a single time slot whether the node isinvolved in control plane functions alone or the node is also involvedin a data transmission/reception.

In an alternate example, to activate as many links as possible whileensuing collision-free scheduling, an OptimizedLink-Activation-Multiple-Access (OLAMA) scheduling protocol is utilized.The OLAMA protocol formulates the scheduling problem into finding amaximal independent set (MIS) in the conflict graph The conflict graphis defined such that the nodes are the data-transmission-links to bescheduled, and there is a link between two nodes in the conflict graphif the corresponding data-transmission-links conflict with each other.In graph theory, an independent set is a set of nodes in a graph, noneof which are adjacent. An independent set is maximal if adding any othernode makes it no longer an independent set. OLAMA utilizes a distributedMIS (DMIS) algorithm, which identifies a MIS of the conflict graph givenall node priorities. The node priorities in OLAMA are calculated thesame way as they are for the link priorities in the LAMA example. In theensuing explanation of OLAMA, the discussion is based on the conflictgraph such that, by “node”, we mean a corresponding “data transmissionlink”.

LAMA can compute the schedule for each slot on the fly because itrequires no packet exchange for the computation. By contrast, it takesmultiple rounds of packet exchange for DMIS to find the schedule (i.e.,MIS) for a given time slot. Furthermore, the wireless channel issusceptible to packet loss. If OLAMA computes the schedule on the fly,significant delays are introduced for data delivery, especially in largenetworks. To reduce the delay incurred by MIS computation whileactivating as many nodes as possible, OLAMA decouples the computation ofMIS from data transmission by precomputing MIS in advance for each timeslot. When a certain slot comes, OLAMA looks up the precomputed MIS onthe fly and activates a node if and only if it is in the MIS. To furtherreduce delay, OLAMA organizes the precomputation of MIS's forconsecutive slots in a pipeline.

In the OLAMA algorithm, as many nodes as possible are activated whilestill ensuring no two neighboring nodes are active simultaneously. Inother words, a maximal independent set of a graph in each slot areactivated through a distributed MIS (DMIS) algorithm, described asfollows.

In DMIS, a node stays in one of three states, UNDECIDED, ACTIVE andINACTIVE, at any given time. In the UNDECIDED state, the node has notdecided whether to join the MIS or not. In the ACTIVE state, the nodejoins the MIS. In the INACTIVE state, the node does not join the MIS.

Initially, all nodes are UNDECIDED. In any slot t, each node computesthe priority of itself and its neighbors according to the followingequation:p _(i)=Hash(i⊕t)⊕i.

In the above equation, i is a node id, Hash(x) is a fast message digestgenerator that returns a random integer by hashing x, and p_(i) is i'spriority. ⊕ concatenates the two operands i and t. The second ⊕guarantees all nodes' priorities are distinct even when Hash( ) returnsthe same number on different inputs. Based on the priority for eachnode, DMIS computes a MIS for slot t in multiple phases. Each phase hasthree steps:

First, a node v exchanges nodal states with its neighbors.

Second, if the node v's priority is higher than all its ACTIVE andUNDECIDED neighbors, it enters the MIS by marking itself ACTIVE;conversely, if any of its higher priority neighbors is ACTIVE, it marksitself INACTIVE.

Third, the node v proceeds to the next phase only if its state remainsUNDECIDED after the second step.

When no node remains UNDECIDED, the algorithm terminates. The resultingMIS is the set containing all ACTIVE nodes, which are to be activated inslot t. It is noteworthy that exchanged nodal state does not includepriority, which is computed locally even for neighbors'. One of skill inthe art will recognize that DMIS's expected running time is O(log(n))phases, where n is the number of nodes in the conflict graph.

The LAMA algorithm requires no packet exchange to compute a schedule andcan thus be called on the fly. In a TDMA setting, a node can call LAMAas a subroutine at the beginning of a slot to instantaneously determineif the node should be active in that slot. Doing the same for DMIS isdifficult because, unlike LAMA, running DMIS requires multiple phasesand each phase incurs delay mainly due to contention to access theshared channel and unreliable channel in the first step. The resultingdelay for data delivery is undesirable for a wide range oftime-sensitive applications.

To reduce DMIS's delay while retaining its high concurrency, the DMIS isdecoupled from data transmission by precomputing MIS of a slot M slotsin advance. The value of M is chosen such that DMIS converges within Mslots, at least with high probability. In slot t, DMIS starts computingMIS for a future slot (t+M) using the nodes' priorities at slot (t+M).The intermediate result, i.e., the current MIS, is stored until slot(t+M). When time reaches slot (t+M), a node simply looks up theprecomputed MIS and decides to become active or inactive, withoutcomputing it on the fly as in LAMA. Since it takes M slots to computethe MIS for each slot, in slot t, MIS's for slot (t+1); (t+2); : : : ;(t+M) are being computed. The computations are organized into apipeline, where the MIS computation of consecutive M slots overlaps.This is more efficient than computing them sequentially. Moreover, avector of M states is aggregated and the states are exchanged in asingle control packet at once, rather than conveying each of the Mstates using a separate packet. This greatly saves channel resources.

FIG. 8 shows an example of the proposed precomputed pipeline in actionwhen M is four. The x-axis 710 denotes time in slot, the y-axis 720denotes the slot whose MIS is being computed. The computation of MIS forslot four starts at slot zero and continues in slots one, two, andthree. In slot four, the MIS has been precomputed and is ready forimmediate activation. Similarly, the MIS for slot five is ready at slotfive, MIS for slot six is ready at slot six, and so on. In slot three,the MIS's for the upcoming slots four, five, six and seven are beingcomputed simultaneously.

The graph changes over time as nodes join or leave the network, linksestablish, or links break. Without further modification, the change inthe graph confuses DMIS because DMIS assumes the graph remains staticbefore it converges. The confusion is removed using a snapshot-basedapproach. Specifically, when starting to compute the MIS for a futureslot (t+M) in slot t, we take a snapshot of the graph and use thesanpshot for the remaining computation even if the graph changes withinM slots. Hence, the graph is consistent for each call of DMIS. Onepotential side effect of this approach is that OLAMA defers the usage ofthe latest graph, which may degrade application performance at upperlayer. Even if degradation occurs, the degradation can be mitigated bymaking M smaller.

A typical embedded device is equipped with limited memory. ImplementingOLAMA on such resource-constrained devices poses an additional challengethat is not found on resource-rich ones. To overcome this challenge, weexpose several key parameters for fine tuning to let upper layer tradeoff between memory usage and performance. There are two places in theOLAMA algorithm and process that can expend significant amount ofmemory, especially when used in a large network.

First, when each node maintains a table containing all its potentialneighbors with size L. To store graph snapshot for each slot, a nodeneeds one bit for each node in its neighbor table, indicating whetherthey interfere or not. It thus costs each node L*M bits to store localgraph snapshots. To reduce the footprint of the snapshot, we takesnapshots every other G slots, instead of every slot. After eachsnapshot, DMIS uses it to compute the MIS of the next G consecutiveslots, where G is a calibration variable determinable by one of skill inthe art. This reduces snapshot footprint by a factor of G. In someinstances, a larger G is not always desirable since the larger G makesthe protocol less agile to graph change. G can be tuned by one of skillin the art to strike a balance between memory consumption and protocolagility.

Second, in the first step of a phase in DMIS, a node exchanges stateswith its neighbors by sending and receiving control packets. The controlpackets can piggyback upper layer payload as well if space permits. Eachslot is further divided into S subslots, out of which one slot isreserved for data packets and the remainder of the slots are reservedfor control packets. Only one data or control packet can be transmittedin each subslot. In total, each node stores L*M intermediate states forpipelined precomputation. On the one hand, because a fixed number ofcontrol packets are needed for the convergence of DMIS for a slot on agiven graph, a larger S packs more control packets into a slot and thuslessens M and memory expenditure. On the other hand, a larger S alsoincreases control overhead and lowers channel utilization for datadelivery. A judicious selection of M again depends on memory consumptionand performance tradeoff, and is determinable by one of skill in theart.

With the above approaches to PRKS protocol, the TDMA scheduling of datatransmissions happens at the beginning of each time slot based on thePRK model information that is readily available in the control plane,hence there is no need for ensuring predictably reliable protocolsignaling on a per transmission basis and thus no delay introduced on aper transmission basis just for protocol signaling. Given that thetimescale of PRKS model adaptation at a link (S, R) is much longer thanthe timescale of individual data transmissions along (S, R), inparticular, the time instants t_(a) and t_(b) for two consecutive PRKmodel adaptations at (S, R) tend to be well separated such that, withinthe early part of the time window [t_(a), t_(b)], the PRK modelparameter of link (S, R) generated at time t_(a) can be reliablydelivered to the relevant nodes and then be used for the TDMA schedulingof data transmissions.

One premise for the correct operation of PRKS is that, for every link(S, R), the sender node S and the receiver node R always use the samePRK model parameters of the relevant links when deciding whether (S, R)should be active in a time slot. Otherwise, node S and node R may derivedifferent conflicting relations between links, and node S may think (S,R) shall be active for this time slot and switch to the data channel totransmit, but node R thinks (S, R) shall be inactive and stays in thecontrol channel, which makes node R unable to receive the transmitteddata from node S and leads to data packet loss.

Since protocol signaling takes time (especially considering theprobabilistic nature of wireless communication) there are time periodswhen node S and node R may have inconsistent information about the PRKmodel parameters in the network. For instance, when node R changes thePRK model parameter K_(S,R,T) _(S,R) (⋅) at time t_(R) by executing theminimum variance controller, the new model parameter K_(S,R,T) _(S,R)(t+1) is known by node R immediately, but it takes time for node R toshare this information with the transmitter node S through protocolsignaling. This delay in protocol signaling is denoted as d_(R,S).Similarly, when another potentially conflicting link (C, D) changes itsPRK model parameter to K_(C,D,T) _(C,D) (t+1), nodes S and R may learnof K_(C,D,T) _(C,D) (t+1) for the first time at different time t′_(S)and t′_(R) respectively, and it may well take time d′_(S,R,C,D) andd′_(R,S,C,D) for S and R to inform each other of their knowledgerespectively. Note that t′_(S) and t′_(R) may be ∞ if nodes S and R donot learn of K_(C,D,T) _(C,D) (t+1) at all.

To address these challenges, the PRKS protocol employs the concept ofactivation time of a PRK model parameter as follows:

When the latest PRK model parameter K_(S,R,T) _(S,R) (t+1) is generatedby receiver node R at time t_(R), the activation time of K_(S,R,T)_(S,R) (t+1) at link (S, R) is defined as t_(R)+d_(R,S). Starting attime t_(R), nodes S and R continue using the previous parameter valueK_(S,R,T) _(S,R) (t) until the activation time t_(R)+d_(R,S) after whichS and R use the parameter value K_(S,R,T) _(S,R) (t+1).

When nodes S and R of the link (S, R) learn of the latest PRK modelparameter K_(C,D,T) _(C,D) (t+1) of a potentially conflicting link (C,D) for the first time at time t′_(S) and t′_(R), respectively, theactivation time of K_(C,D,T) _(C,D) (t+1) at link (S, R) is defined asmin{t′_(S)+d′_(S,R,C,D),t′_(R)+d′_(R,S,C,D)}. Nodes S and R continueusing the PRK model parameter K_(C,D,T) _(C,D) (t) until the activationtime min{t′_(S)+d′_(S,R,C,D),t′_(R)+d′_(R,S,C,D)}, after which nodes Sand R use the parameter value K_(C,D,T) _(C,D) (t+1).

With this approach, the sender-receiver (S-R) consistency is guaranteedsuch that the sender and the receiver of a link always use the same PRKmodel parameters in the TDMA scheduling. In practice, the protocolsignaling delays d_(R,S), d′_(S,R,C,D), and d′_(R,S,C,D) are all randominstead of deterministic, and we can use their upper-quantile values(e.g., maximum or 0.9 quantile) in defining activation time. In oneimplementation of the PRKS protocol, the 0.95 quantiles of the signalingdelays are used.

An alternative approach to addressing the transient informationconsistency between a sender S and its receiver R is as follows based onsender-receiver coordination.

If the link (S, R) shall be active in a time slot to, the sender Scomputes the time slot t₁ when the link (S, R) will be active again thenext time. Then the sender S piggybacks the value of t₁ onto the datapacket, if any, to be transmitted to the receiver R at to as well asonto every protocol signaling packet that the sender S may transmitduring t₀ and t₁. If the receiver R receives a data or a protocolsignaling packet from the sender S showing that the sender S willtransmit in a future time slot t₁, the receiver R will stay in the datachannel at t₁ even if the local execution of the LAMA (or OLAMA)algorithm at R may show link (S, R) as inactive at t₁.

After computing at time to the next time slot t₁ to transmit to thereceiver R, the sender S will not transmit to the receiver R at any timeslot t₁′∈(t₀, t₁) unless the receiver R tells the sender S to transmitat t₁′ as we discuss next. This rule applies even if the local PRK modelparameters at the sender S shows at slot t₁′ that the link (S, R) shallbe active at this time slot. This rule implicitly introduces delay inusing the latest information on PRK model parameters, but this delay issignificantly less than a delay in an approach based on perfectconsistency between the sender S and the receiver R as we havediscussed.

After the receiver R learns that the sender S will transmit in a futuretime slot t₁, if the execution of the LAMA (or OLAMA) algorithm at thereceiver R at a time slot t₀′ shows that the link (S, R) shall be activeat a time slot t₁′<t₁ and if the time window of [t₀′, t₁′] is longenough for the receiver R to successfully inform the sender S of thevalue of t₁′ with high probability, the receiver R changes its localvalue of t₁ to t₁′ and piggybacks the value of t₁′ onto packets (e.g.,protocol signaling packets) that the receiver R may transmit during[t₀′, t₁′). If the sender S receives a packet from the receiver Rshowing that link (S, R) shall be active at a future time slot t₁′<t₁,the receiver R changes the value of t₁ to t₁′. This rule is toameliorate the implicit delay in using the latest PRK parameter valuesthat the previous rule may introduce.

If the receiver R does not receive any data packet from the sender S atthe time slot t₁ (e.g., due to data packet loss), the receiver R entersand stays in the “conservative” state until it receives a packet fromthe sender S again showing that (S, R) shall be active at another futuretime slot. While in the “conservative” state, the receiver R stays inthe data channel for a time slot t₂ as long as, at t₂, the link (S, R)has higher priority to be active than other links associated with thereceiver R. The conservative state ensures that the receiver R is in thedata channel whenever the sender S transmits a data packet to thereceiver R, and it enables the receiver R to be synchronized with thesender S on the data-transmission schedule again.

In the system boot-up phase, the sender S executes the basic LAMA (orOLAMA) algorithm and the receiver R remains in the conservative statewhen deciding whether to stay in the data or control channel for a timeslot, until the sender S transmits for the first time and the receiver Rreceives a first packet from the sender S respectively.

With the above coordination mechanism, the receiver R is guaranteed tobe in the data channel whenever the sender S transmits data packets. Inthe meantime, the receiver R stays in the control channel often enoughto be updated with the latest PRK model parameters of close-by links.

The aforementioned coordination between a sender S and its receiver R isthe only inter-node coordination needed to address the potentialinconsistency on the PRK model parameters during transient periods. Inparticular, perfect information consistency that requires the same PRKmodel parameter of a link (S, R) is not necessary to be used by link (S,R) and all the links whose transmitters are in the exclusion regionaround receiver R. That is, as long as the sender-receiver consistencyis ensured, a node can use the new PRK model parameter of a link themoment the node learns of the parameter. Thus, as long as thesender-receiver consistency is ensured, the earliest use of new PRKmodel parameters helps improve data delivery reliability when thecorresponding exclusion regions expand, or it helps improve the spatialreuse and concurrency of data transmissions when the correspondingexclusion regions shrink.

While the above disclosure has focused on the exclusion regions aroundreceivers alone, it is understood that similar processes and methodswould apply to transmitters (senders). If it is important to ensure ACKreliability at the link layer (e.g., for avoiding unnecessaryretransmissions), similar approaches to protecting data receptions canbe applied to protect ACK receptions by maintaining an exclusion regionaround the transmitter of each link.

The above disclosure focuses on unicast where a sender S wants totransmit data packets to a receiver R. The mechanisms presented above,however, are readily extendable to enable broadcast/multicast where asender S wants to reliably transmit data packets to a set of receiversat the same time. In the control plane, for instance, the sender S canuse a high-power signal to explicitly identify the exclusion regionsaround the set of receivers so that no node in any of the exclusionregions transmits concurrently with the broadcast/multicast transmissionby the sender S.

The above discussion is also directed toward mostly static wirelesssensing and control networks. The basic mechanisms presented above arereadily extendable to support predictable link reliability in mobilewireless sensing and control networks such as for groups of vehicles. Inmobile wireless sensing and control networks, node mobility introducesdynamics in node spatial distribution and thus dynamics in wirelesscommunication. In particular, dynamics in node spatial distributionincreases the dynamics in signal power attenuation between nodes, whichchallenges the maintenance of local signal maps and the adaptation ofthe PRK model.

To address the challenge in such a network, the following facts can beleveraged: 1) The timescale of non-negligible node movement (e.g.,vehicle movement on highways) is in seconds, while the timescale ofwireless communication is in milliseconds or microseconds, thesignificantly longer timescale of the physical movement of nodes enablesnodes to exchange control signals for PRK-based scheduling adaptation onthe fly, and 2) There are well-established, microscopic mobility modelsfor node movement (e.g., vehicle movement), these models help estimatedynamics in node spatial distribution and thus dynamics in signal powerattenuation between nodes. Therefore, these models can help enablepredictive adaptation of the signal map and the PRK model.

It is further understood that any of the above described concepts can beused alone or in combination with any or all of the other abovedescribed concepts. Although an embodiment of this invention has beendisclosed, a worker of ordinary skill in this art would recognize thatcertain modifications would come within the scope of this invention. Forthat reason, the following claims should be studied to determine thetrue scope and content of this invention.

The invention claimed is:
 1. A distributed sensing and control networkcomprising: a plurality of sensing and control nodes, each of saidsensing and control nodes including a sensor, a local controller, alocal memory and a wireless transmitter/receiver; each local memorystores instantiated physical-ratio-constant (PRK) interference models, acorresponding local signal map and instructions for implementing aphysical-ratio-constant (PRK) scheduling protocol (PRKS) for wirelesstransmissions, the stored physical-ratio-constant-scheduling (PRKS)protocol includes defining an interference relationship between thesensing and control node storing the local signal map and each othersensing and control node of said plurality of sensing and control nodeswithin an exclusion region of the sensing and control node storing thelocal signal map.
 2. The distributed sensing and control network ofclaim 1, wherein said local memory further stores instructions forcausing said controller to determine a physical-ratio-K-constant (PRK)parameter for each link between a receiving sensing and control node andeach other sensing and control node in said distributed sensing andcontrol network.
 3. The distributed sensing and control network of claim2, wherein said local signal map further stores the determined PRKparameter for each link between a receiving sensing and control node andeach other sensing and control node in said distributed sensing andcontrol network.
 4. The distributed sensing and control network of claim1, wherein each of said interference relationships is based on at leastone PRK parameter.
 5. The distributed sensing and control network ofclaim 1, wherein said local memory further includes instructions forcausing said local controller to define an exclusion region for saidreceiving sensing and control node wherein a concurrent sensing andcontrol node is within the exclusion region when an expected power ofdata packet signals from said concurrent sensing and control node tosaid receiving sensing and control node is greater than or equal to anexpected power of data packet signals from a sending sensing and controlnode to the receiving sensing and control node divided by the PRK modelparameter of the receiving sensing and control node.
 6. The distributedsensing and control network of claim 5, wherein each of saidinterference relationships is defined based at least partially on saidexclusion regions.
 7. The distributed sensing and control network ofclaim 1, wherein each of said sensors includes a data transmissionwireless channel and a PRKS protocol signaling wireless channel, each ofsaid data transmission wireless channel and said PRKS protocol signalingwireless channel having different transmission frequencies.
 8. Thedistributed sensing and control network of claim 1, wherein a portion ofsaid sensing and control nodes are mobile wireless sensing and controlnodes.
 9. The distributed sensing and control network of claim 8,wherein a spatial distribution of said mobile wireless sensing andcontrol nodes is not constant.
 10. The distributed sensing and controlnetwork of claim 9, wherein signal power attenuation between sensing andcontrol nodes in the distributed sensing and control network is notconstant.
 11. The distributed sensing and control network of claim 8,wherein a node movement timescale is larger than a wirelesscommunication timescale.
 12. The distributed sensing and control networkof claim 1, wherein said local memory stores instructions for causingsaid sensing and control node to schedule transmission in thedistributed sensing and control network based on said interferencerelationships.
 13. The distributed sensing and control network of claim1, wherein said local memory further comprises instructions causing alocal controller of a sending sensing and control node to schedule atransmission from said sending sensing and control node to a pluralityof receiving sensing and control nodes simultaneously.